In this study, we found that carriers of a common variant in the autism risk gene, CNTNAP2,
had differences in structural brain connectivity computed from high-field DTI. Graph theory measures differed in individuals homozygous for the risk allele. This higher-risk group had shorter CPL in the whole-brain network, greater SW and greater EGLOB in the left hemisphere, and greater EGLOB in the right hemisphere. These results may seem counter-intuitive given findings of higher efficiency, but higher efficiency in structural networks may reflect more random connections in the risk-group's brain networks, as random networks have high levels of EGLOB (Bullmore and Sporns, 2009
). Further analysis at the nodal level revealed that the homozygous at-risk participants had lower ECC across 60 of the 70 network nodes in the nonrisk participants, and borderline significant results (passed p
<0.05 but not FDR correction) in EREG in 11 of the 70 nodes. A final analysis attempted to further simplify the results by assessing FA and fiber density differences, but did not detect associations with these more common fiber measures. In other words, several global and nodal properties of the structural network were different in carriers of the risk gene, but they were not attributable to more common characteristics of fibers, such as fiber density or FA. A larger sample size might detect differences in FA in carriers of the risk gene, but our findings suggest that differences are more prominent at the network level.
In their recent study, Scott-Van Zeeland et al. (2010
) found that a CNTNAP2
SNP was associated with differences in the functional connectivity of frontal and parietal cortical networks, including effects on the strength of short- and long-range connections to the frontal and parietal cortex. In this case, the range reflected the physical distance between two regions, while in graph theory, distance instead reflects the number of paths between one node and another. While path distance and physical distance are not the same, they both indicate distance between one brain region and another. Since this is the property measured by CPL and EGLOB using graph theoretical methods, we hypothesized that we could assess corresponding measures from structural networks using DTI, and that these measures might be altered in carriers of the CNTNAP2
risk allele. We found that carriers have altered structural connectivity—as measured by a number of graph theory metrics—which may partly underlie the alterations in functional connectivity.
SW is a well-developed concept from graph theory (Watts and Strogatz, 1998
) that has more recently been applied to brain networks (Sporns et al., 2004
). A network with high SW has high local clustering and a short CPL. Subjects homozygous for the risk allele had greater SW and greater EGLOB in their left hemispheres, which are both driven in part or wholly by shorter CPLs. Risk subjects also had higher EGLOB in the right hemisphere as well as shorter CPL at a whole-brain level. Since there were no significant differences in clustering, differences in path length may drive the observed differences in SW. Greater efficiency in those at risk is unexpected, as Hagmann et al. (2010
) found greater efficiency as development progressed, and Pollonini et al. (2010
) found decreased EGLOB in autistic subjects. However, Hagmann et al. based their calculations on 1/ADC, while we based ours on fiber density, and Pollonini et al. was a magnetoencephalography (MEG) study with Granger causality, so the comparison is not direct. A random network has high efficiency (Bullmore and Sporns, 2009
), but it may not be functionally advantageous if the proper connections are not made. Neural network complexity is typically achieved by a balance of randomness and regularity—at either extreme, you have a system less able to learn, because it is either never stable enough to remember or never flexible enough to adapt (Sporns, 2011
). A more random network, while having a shorter average path length, will be less complex, and arguably further from ideal in terms of brain function. A more random network, while having a shorter average path length, will be less complex, and may not reflect the organization found in real functional brain networks. Individuals differ widely in brain structure and function, but complete “randomness” of connections is not typical of functional circuitry in the brain. A random network, with no stability in time or logical set up, does not tend to make the most efficient use of the brain's resources (Chialvo, 2010
). While additional studies are required, higher EGLOB may reflect more random connections in the structural networks of the at-risk participants, as random networks have low path lengths.
Based on our global results, we decided to look further into various nodal measures of connectivity. In these post hoc
tests, we found a significant association between CNTNAP2
allele dose and the ECC at 60 of the 70 nodes, with nonrisk carriers having greater ECC across all nodes. ECC is the distance, in paths traversed, between a given node and the node farthest from it (Sporns, 2002
). Nonrisk participants had greater ECC across most of the brain. Studies of ECC in brain networks are few (Pollonini et al., 2010
) and have not generated any significant results so far; so, we have little context for these results. However, given that they are across a majority of nodes in the brain, they could underlie the global trends we found as well. We found 11 nodes with borderline significant differences (passed p
<0.05 but not FDR correction) in EREG, 8 of which were in the frontal lobe, 2 in the temporal lobe, and 1 in the parietal lobe. These are the areas where CNTNAP2
expression is especially enriched (Abrahams et al., 2007
; Arking et al., 2008
; Strauss et al., 2006
; Vernes et al., 2008
) and where Scott-Van Zeeland found differences in functional connectivity.
In attempting to discover a simpler underlying cause of these results, we looked into possible differences in the fiber density matrices of the two groups. We had initially ruled out differences in overall connectivity by running our analysis of CNTNAP2
on the whole fiber density matrices. However, in trying to understand our results of greater EGLOB and shorter CPL in the risk allele carriers, we decided to look only at those connections with at least one terminus in the frontal, parietal, or temporal lobes. While we found a trend for greater fiber density in the nonrisk subjects in a large number of frontal, parietal, and temporal connections, these results did not pass FDR correction. Tan et al. (2010
) conducted a study of a different CNTNAP2
SNP, rs7794745, in a large cohort of healthy subjects as well. Regional gray and white matter volumes were lower in those homozygous for the risk allele. We will continue to search for an explanation for our unexpected findings, but currently they do not appear to be reducible to more simple measures of structural connectivity.
Our findings relating a common risk variant in CNTNAP2
with structural connectivity suggests that the protein it codes for, CASPR2, may be involved in white matter tract structure. This seems likely, as CASPR2 has a role in neuroblast migration (Strauss et al., 2006
) and in stabilizing K+
channels in the juxtaparanodal region (Poliak et al., 1999
risk allele carriers may have aberrant neuroblast migration or K+
channel clustering early in development; this may even underlie the differences we see in structural connectivity. Abnormal neuronal migration early in development could lead to altered development of white matter, leading to the changes we see. Abnormal K+
channel clustering could affect axonal physiology for developing tracts, perhaps even affecting overall tract structure. The recent study characterizing the CNTNAP2
knockout found, along with various behavioral hallmarks of autism, neuronal migration abnormalities, including abnormal clustering of neurons in the deep layers of the cortex (Peñagarikano et al., 2011
is a risk gene for autism, but it also has effects in nonautistic populations with language disorders. It may be more appropriate to consider it as a risk gene for language difficulties—a key component of autism. A disorder as complex and varied as autism most likely results from a constellation of genetic variations interacting with environmental influences (Szatmari et al., 2007
). The SNP rs2710102 in CNTNAP2
may be one of these polymorphisms that, when combined with others, could increase te risk for autism by increasing the susceptibility to language difficulties. In this article, our focus was the effects of CNTNAP2
on brain structural connectivity. Understanding why a gene increases risk for a disorder is as crucial as determining that it increases risk in the first place, as a more mechanistic understanding is necessary for ultimately developing interventions. Here, we discovered a mechanistic clue that might explain the association between CNTNAP2
and autism and language disorders. This altered connectivity may represent an intermediate phenotype for one source of language difficulties. Our participants were a large cohort of twins screened for psychiatric disorders and developmental conditions; thus, they fall within the normal range of language ability.
Of the three different models, the recessive model yielded the strongest results. We chose this model based on information that individuals with the CC genotype have an increased risk of language impairment (www.snpedia.com/index.php/Rs2710102
). However, Scott-Van Zeeland's study supports a dominant effect of the CNTNAP2
SNP. Vernes et al. (2008
) found that a haplotype of nine SNPs, including this CNTNAP2
SNP, had a dominant effect, but no other studies have produced evidence on the dominance of CNTNAP2
rs2710102 by itself. Our analyses were based on healthy subjects, while previous studies have been conducted on autistic or language-impaired participants, so we followed our analyses with post hoc
tests to check the other two models in case the effect differed from that in our healthy population.